Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

Abstract

Many computer vision applications needs to detect moving object from an input video sequences. The main applications of this are traffic monitoring, visual surveillance, people tracking and security etc. Among these, traffic monitoring is one of the most difficult tasks in real time video processing. Many algorithms are introduced to monitor traffic accurately. But most of the cases, the detection accuracy is very less and the detection time is higher which makes the algorithms are not suitable for real time applications. In this paper, a new technique to detect moving vehicle efficiently using Modified Gaussian Mixture Model and Modified Blob Detection techniques is proposed. The modified Gaussian Mixture model generates the background from overall probability of the complete data set and by calculating the required step size from the frame differences. The modified Blob Analysis is then used to classify proper moving objects. The simulation results shows that the method accurately detect the target

Efficient reconfigurable architecture for moving object detection with motion compensation

“…The designed architecture is simulated by taking three different scenarios low density traffic, high density traffic and video steam captured using moving camera to evaluate the detection accuracy of the system. The statistical parameters such as detection accuracy, true detection rate, false detection rate and not detected rate [22] are calculated to validate the designed architecture.  ISSN: 2502-4752 Figures 9-11 shows one random frame of the entire simulation for normal traffic, dense traffic and video taken from moving camera respectively.…”